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Sensors 2016, 16(9), 1431; doi:10.3390/s16091431

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Computer Science and Engineering, Kyung Hee University, 1732 Deokyoungdaero, Gilheung-gu, Yongin-si 446-701, Korea
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 23 July 2016 / Revised: 24 August 2016 / Accepted: 30 August 2016 / Published: 6 September 2016
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. View Full-Text
Keywords: ambient assisted living; web of objects; mental healthcare; emergency psychiatry; smart home ambient assisted living; web of objects; mental healthcare; emergency psychiatry; smart home

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alam, M.G.R.; Abedin, S.F.; Al Ameen, M.; Hong, C.S. Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction. Sensors 2016, 16, 1431.

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